{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "from tqdm import tqdm\n",
        "import random\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import gymnasium as gym\n",
        "import gym_anytrading\n",
        "\n",
        "from stable_baselines3 import A2C, PPO\n",
        "from stable_baselines3.common.callbacks import BaseCallback\n",
        "\n",
        "import torch"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create Env"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "env_name = 'stocks-v0'  # or 'forex-v0'\n",
        "env = gym.make(env_name)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Define Functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "def print_stats(reward_over_episodes):\n",
        "    \"\"\"  Print Reward  \"\"\"\n",
        "\n",
        "    avg = np.mean(reward_over_episodes)\n",
        "    min = np.min(reward_over_episodes)\n",
        "    max = np.max(reward_over_episodes)\n",
        "\n",
        "    print (f'Min. Reward          : {min:>10.3f}')\n",
        "    print (f'Avg. Reward          : {avg:>10.3f}')\n",
        "    print (f'Max. Reward          : {max:>10.3f}')\n",
        "\n",
        "    return min, avg, max\n",
        "\n",
        "\n",
        "# ProgressBarCallback for model.learn()\n",
        "class ProgressBarCallback(BaseCallback):\n",
        "\n",
        "    def __init__(self, check_freq: int, verbose: int = 1):\n",
        "        super().__init__(verbose)\n",
        "        self.check_freq = check_freq\n",
        "\n",
        "    def _on_training_start(self) -> None:\n",
        "        \"\"\"\n",
        "        This method is called before the first rollout starts.\n",
        "        \"\"\"\n",
        "        self.progress_bar = tqdm(total=self.model._total_timesteps, desc=\"model.learn()\")\n",
        "\n",
        "    def _on_step(self) -> bool:\n",
        "        if self.n_calls % self.check_freq == 0:\n",
        "            self.progress_bar.update(self.check_freq)\n",
        "        return True\n",
        "    \n",
        "    def _on_training_end(self) -> None:\n",
        "        \"\"\"\n",
        "        This event is triggered before exiting the `learn()` method.\n",
        "        \"\"\"\n",
        "        self.progress_bar.close()\n",
        "\n",
        "\n",
        "# TRAINING + TEST\n",
        "def train_test_model(model, env, seed, total_num_episodes, total_learning_timesteps=10_000):\n",
        "    \"\"\" if model=None then execute 'Random actions' \"\"\"\n",
        "\n",
        "    # reproduce training and test\n",
        "    print('-' * 80)\n",
        "    obs = env.reset(seed=seed)\n",
        "    torch.manual_seed(seed)\n",
        "    random.seed(seed)\n",
        "    np.random.seed(seed)\n",
        "\n",
        "    vec_env = None\n",
        "\n",
        "    if model is not None:\n",
        "        print(f'model {type(model)}')\n",
        "        print(f'policy {type(model.policy)}')\n",
        "        # print(f'model.learn(): {total_learning_timesteps} timesteps ...')\n",
        "\n",
        "        # custom callback for 'progress_bar'\n",
        "        model.learn(total_timesteps=total_learning_timesteps, callback=ProgressBarCallback(100))\n",
        "        # model.learn(total_timesteps=total_learning_timesteps, progress_bar=True)\n",
        "        # ImportError: You must install tqdm and rich in order to use the progress bar callback. \n",
        "        # It is included if you install stable-baselines with the extra packages: `pip install stable-baselines3[extra]`\n",
        "\n",
        "        vec_env = model.get_env()\n",
        "        obs = vec_env.reset()\n",
        "    else:\n",
        "        print (\"RANDOM actions\")\n",
        "\n",
        "    reward_over_episodes = []\n",
        "\n",
        "    tbar = tqdm(range(total_num_episodes))\n",
        "\n",
        "    for episode in tbar:\n",
        "        \n",
        "        if vec_env: \n",
        "            obs = vec_env.reset()\n",
        "        else:\n",
        "            obs, info = env.reset()\n",
        "\n",
        "        total_reward = 0\n",
        "        done = False\n",
        "\n",
        "        while not done:\n",
        "            if model is not None:\n",
        "                action, _states = model.predict(obs)\n",
        "                obs, reward, done, info = vec_env.step(action)\n",
        "            else: # random\n",
        "                action = env.action_space.sample()\n",
        "                obs, reward, terminated, truncated, info = env.step(action)\n",
        "                done = terminated or truncated\n",
        "\n",
        "            total_reward += reward\n",
        "            if done:\n",
        "                break\n",
        "\n",
        "        reward_over_episodes.append(total_reward)\n",
        "\n",
        "        if episode % 10 == 0:\n",
        "            avg_reward = np.mean(reward_over_episodes)\n",
        "            tbar.set_description(f'Episode: {episode}, Avg. Reward: {avg_reward:.3f}')\n",
        "            tbar.update()\n",
        "\n",
        "    tbar.close()\n",
        "    avg_reward = np.mean(reward_over_episodes)\n",
        "\n",
        "    return reward_over_episodes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Train + Test Env"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "tags": []
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "env_name                 : stocks-v0\n",
            "seed                     : 42\n",
            "--------------------------------------------------------------------------------\n",
            "RANDOM actions\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Episode: 40, Avg. Reward: 284.550: 100%|██████████| 50/50 [00:01<00:00, 49.99it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Min. Reward          :    284.550\n",
            "Avg. Reward          :    284.550\n",
            "Max. Reward          :    284.550\n",
            "--------------------------------------------------------------------------------\n",
            "model <class 'stable_baselines3.a2c.a2c.A2C'>\n",
            "policy <class 'stable_baselines3.common.policies.ActorCriticPolicy'>\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "model.learn(): 100%|██████████| 25000/25000 [00:25<00:00, 964.23it/s] \n",
            "Episode: 40, Avg. Reward: 565.404: 100%|██████████| 50/50 [00:36<00:00,  1.36it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Min. Reward          :     53.153\n",
            "Avg. Reward          :    550.280\n",
            "Max. Reward          :    963.165\n",
            "--------------------------------------------------------------------------------\n",
            "model <class 'stable_baselines3.ppo.ppo.PPO'>\n",
            "policy <class 'stable_baselines3.common.policies.ActorCriticPolicy'>\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "model.learn(): 26600it [00:21, 1232.28it/s]                           \n",
            "Episode: 40, Avg. Reward: 646.742: 100%|██████████| 50/50 [00:37<00:00,  1.34it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Min. Reward          :    346.549\n",
            "Avg. Reward          :    651.505\n",
            "Max. Reward          :   1128.497\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "seed = 42  # random seed\n",
        "total_num_episodes = 50\n",
        "\n",
        "print (\"env_name                 :\", env_name)\n",
        "print (\"seed                     :\", seed)\n",
        "\n",
        "# INIT matplotlib\n",
        "plot_settings = {}\n",
        "plot_data = {'x': [i for i in range(1, total_num_episodes + 1)]}\n",
        "\n",
        "# Random actions\n",
        "model = None \n",
        "total_learning_timesteps = 0\n",
        "rewards = train_test_model(model, env, seed, total_num_episodes, total_learning_timesteps)\n",
        "min, avg, max = print_stats(rewards)\n",
        "class_name = f'Random actions'\n",
        "label = f'Avg. {avg:>7.2f} : {class_name}'\n",
        "plot_data['rnd_rewards'] = rewards\n",
        "plot_settings['rnd_rewards'] = {'label': label}\n",
        "\n",
        "learning_timesteps_list_in_K = [25]\n",
        "# learning_timesteps_list_in_K = [50, 250, 500]\n",
        "# learning_timesteps_list_in_K = [500, 1000, 3000, 5000]\n",
        "\n",
        "# RL Algorithms: https://stable-baselines3.readthedocs.io/en/master/guide/algos.html\n",
        "model_class_list = [A2C, PPO]\n",
        "\n",
        "for timesteps in learning_timesteps_list_in_K:\n",
        "    total_learning_timesteps = timesteps * 1000\n",
        "    step_key = f'{timesteps}K'\n",
        "\n",
        "    for model_class in model_class_list:\n",
        "        policy_dict = model_class.policy_aliases\n",
        "        # https://stable-baselines3.readthedocs.io/en/master/guide/custom_policy.html\n",
        "        # MlpPolicy or MlpLstmPolicy\n",
        "        policy = policy_dict.get('MlpPolicy')\n",
        "        if policy is None:\n",
        "            policy = policy_dict.get('MlpLstmPolicy')\n",
        "        # print ('policy:', policy, 'model_class:', model_class)\n",
        "\n",
        "        try:\n",
        "            model = model_class(policy, env, verbose=0)\n",
        "            class_name = type(model).__qualname__\n",
        "            plot_key = f'{class_name}_rewards_'+step_key\n",
        "            rewards = train_test_model(model, env, seed, total_num_episodes, total_learning_timesteps)\n",
        "            min, avg, max, = print_stats(rewards)\n",
        "            label = f'Avg. {avg:>7.2f} : {class_name} - {step_key}'\n",
        "            plot_data[plot_key] = rewards\n",
        "            plot_settings[plot_key] = {'label': label}     \n",
        "                   \n",
        "        except Exception as e:\n",
        "            print(f\"ERROR: {str(e)}\")\n",
        "            continue"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Plot Results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "data = pd.DataFrame(plot_data)\n",
        "\n",
        "sns.set_style('whitegrid')\n",
        "plt.figure(figsize=(8, 6))\n",
        "\n",
        "for key in plot_data:\n",
        "    if key == 'x':\n",
        "        continue\n",
        "    label = plot_settings[key]['label']\n",
        "    line = plt.plot('x', key, data=data, linewidth=1, label=label)\n",
        "\n",
        "plt.xlabel('episode')\n",
        "plt.ylabel('reward')\n",
        "plt.title('Random vs. SB3 Agents')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "p3.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
